Journal of Magnetic Resonance Imaging

Introduction: Structural neuroimaging relies on T1-weighted (T1w) magnetic resonance imaging (MRI) for brain morphometry, yet at 7 Tesla (7 T) transmit field (B1+) inhomogeneity remains a major source of bias. Although Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) improves the tissue contrast, residual B1+ effects may persist and may be exacerbated in aging or clinical populations, where anatomical and physiological factors further challenge image quality and preprocessing. The impact of B1+ inhomogeneity on automated quality assessment and morphometric statistical inference remains insufficiently understood. Methods: Submillimeter 7 T MP2RAGE brain acquisitions from carriers of a mitochondrial gene mutation (m.3243A>G) and controls were retrieved from previous studies. Image quality before and after B1+ inhomogeneity correction was assessed by multiple automated pipelines. Case-control morphometric studies, including regional volume and mean cortical thickness, were analyzed in both registration based and deep learning based segmentation frameworks. Changes in image quality metrics (IQMs) and morphometric statistical significance were evaluated to determine the impact of B1+ inhomogeneity correction. Results: Overall image quality rating and metrics sensitive to intensity non-uniformity and topological integrity consistently improved after B1+ inhomogeneity correction. However, its impact on morphometric statistical inferences was strongly method-dependent. Some pipelines showed redistribution of significant regions, whereas others predominantly demonstrated increased effects in sensitivity. Across methods, B1+ inhomogeneity correction altered the findings of morphometric analyses, particularly in cortical regions. Conclusion: Residual B1+ inhomogeneity at 7 T substantially influences both image quality control and morphometric evaluations. Current automated quality control approaches can hardly capture these effects reliably. B1+ inhomogeneity correction will not only improve intensity uniformity, but also change sensitivity of morphometric statistical inferences. To establish reliable morphometric biomarkers at UHF strengths, explicit B1+ correction and customized preprocessing are practically necessary and highly recommended.

PURPOSE: Alzheimer disease (AD) is associated with cognitive impairment, brain atrophy, and elevated amyloid-beta and tau. The study aimed to characterize regional atrophy associated with elevated amyloid-beta and tau, as measured by [18F]florbetapir (FBP) and [18F]flortaucipir (FTP) positron emission tomography (PET), respectively, and determine whether combining PET and atrophy data improves the prediction of cognitive impairment. METHODS: Alzheimer Disease Neuroimaging Initiative data (n = 381) were retrospectively analyzed. PET results were correlated with cortical thickness, gray matter (GM) volumes, Mini-Mental State Examination, and Montreal Cognitive Assessment. Linear/logistic regression and area under the curve (AUC) were used to evaluate for significant correlations and compare performances in distinguishing cognitive impairment, respectively. RESULTS: Incremental loss of cortical thickness and GM volume was observed from FBP-/FTP- (n = 205) to single PET-positive (FBP+/FTP-, n = 133; FBP-/FTP+, n = 5) and FBP+/FTP+ (n = 38) groups, particularly in the temporal and parietal lobes. FBP+/FTP+ showed the most severe cortical thickness loss in the entorhinal cortex, temporal lobe GM atrophy, and cognitive impairment. Adding brain atrophy as the third variable resulted in higher odds ratios and improved AUCs for cognitive impairment, with FBP+/FTP+/temporal GM or entorhinal cortical atrophy+ demonstrating the strongest associations with cognitive impairment. CONCLUSION: A multimodal approach combining PET and MRI may help improve the assessment of cognitive impairment in AD.

Background. Lung metastases in osteosarcoma (OS) are the main cause of the death. The accuracy of the diagnosis of nodules by computed tomography (CT) of the lungs is critically important for determining the disseminated stage of the disease and planning surgical treatment. The use of artificial intelligence (AI) in the search for lung nodules increases the accuracy of diagnosis and reduces the chance of missing metastases. Objective: to evaluate the accuracy of lung nodules diagnosis in adolescents with OS using AI. Methods. A retrospective assessment of CT scans of adolescents with OS was performed. A pathological nodule with an average size of [≥]4 mm was considered a target finding. The diagnostic accuracy of an AI algorithm previously trained on an adult dataset was evaluated, and the number of false positives (FP) and false negatives (FN) was determined. Sensitivity, specificity, accuracy, area under the ROC curve (AUC), positive predictive value, negative predictive value, and F1-measure were calculated. Based on the obtained results, the effectiveness of the algorithm was assessed. Results. 248 CT scans of adolescents with OS were evaluated. The following results were obtained: in 5 cases, the AI algorithm showed a FP result (2.02%), in 34 cases, it showed a FN result (13.71%), and in 209 cases, a correct result (both true positive and true negative) (84.27%). The diagnostic accuracy of the algorithm was 0.843 (95% CI 0.794-0.887). The application of the AI algorithm in the practice of an X-ray doctor in a specific clinical task would allow to increase the sensitivity from 0.805 to 0.891, while ensuring an absolute decrease in the number of FN results by 8.59% and a relative decrease by 44%. Conclusion. The obtained results confirm the practical value of the application of the AI algorithm and justify the implementation of AI-assisted systems in the diagnostic protocols for lung metastases in adolescents with OS.

Background: Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype characterized by the absence of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 expression. It is associated with limited targeted treatment options, early relapse, and a high propensity for visceral metastasis. Data describing metastatic patterns and treatment characteristics of TNBC in Nigeria remain limited. Methods: This retrospective descriptive cohort study included 869 patients with TNBC managed at the Medserve-LUTH Cancer Center, Lagos University Teaching Hospital, Nigeria between June 2019 and June 2024. Demographic, clinicopathologic, metastatic, and treatment-related data were extracted from electronic medical records. Descriptive statistics were used to summarize patient characteristics, metastatic patterns, and treatment profiles. Associations between metastatic disease and selected clinicopathologic and treatment variables were explored using Pearsons chi-square test. Complete-case analysis was applied throughout. Results: The mean age at presentation was 52.09 {+/-} 12.26 years. Most patients were married (79.1%), postmenopausal (64.3%), and of Yoruba ethnicity (56.8%). Advanced disease predominated, with Stage III and Stage IV disease accounting for 42.9% and 35.6% of cases, respectively. Invasive ductal carcinoma was the most common histologic subtype (77.0%), while Grade II tumours constituted 51.3% of graded cases. Surgery was performed in 73.1% of patients, predominantly mastectomy (70.9% of surgical procedures). Chemotherapy was administered to 83.2% of patients, most commonly anthracycline-based regimens (41.8%), while radiotherapy was delivered to 63.5% of patients, with hypofractionated schedules of 42-43 Gy in 15-16 fractions accounting for 47.2% of radiotherapy courses. Metastatic disease was documented in 32.9% of evaluable patients. Lung metastasis was the most frequent site (62.5%), followed by bone (46.3%), regional lymph node invasion (38.5%), liver (23.0%), and brain (22.6%). Tumour grade and histologic subtype were not significantly associated with metastatic disease, whereas radiotherapy exposure demonstrated a significant association with metastatic status ({chi}{superscript 2} = 10.35, p = 0.001). Conclusion: TNBC in this Nigerian cohort was characterized by advanced-stage presentation, invasive ductal predominance, extensive use of multimodality treatment, and substantial visceral metastatic burden. Lung metastasis was the most common metastatic site. These findings provide contemporary real-world data on TNBC in Nigeria and highlight the continuing need for earlier diagnosis, timely referral, and sustained investment in comprehensive cancer care services.

Background. Tethered cord syndrome (TCS) is classically associated with a low-lying conus medullaris, yet many surgically treated children have a normally positioned conus (occult TCS). Large-scale normative data on conus position in children, and the diagnostic value of quantitative conus assessment, are limited. Purpose. To establish a large-cohort reference distribution for conus medullaris termination level in children, to quantify conus position in children surgically treated for presumed (occult) TCS, and to test whether automated conus segmentation and radiomics can distinguish TCS from normal. Materials and Methods. In this retrospective single-center study, conus termination level was extracted from structured radiology reports of consecutive pediatric lumbosacral MRI examinations and encoded numerically (L1 = 1, L2 = 2, etc.). Children surgically treated for tethered cord were identified by linkage to an operative registry (name and date of birth) and restricted to preoperative examinations. A deep-learning model (nnU-Net) was trained for conus segmentation on axial T2-weighted images. IBSI-compliant radiomic features were extracted; reproducibility was assessed by intra- and inter-observer intraclass correlation (ICC). A case-control radiomics analysis used batch-only ComBat harmonization and cross-validated L1-penalized logistic regression; discrimination was compared with conus level by paired bootstrap. Results. Among 9,808 examinations with a parseable conus level (98.5% of reports; parser validated against dual blinded annotation, 99.4% agreement, weighted kappa 0.946), the conus terminated in the L1 region in 85.7% and the L2 region in 14.3% of the reference cohort (postoperative examinations excluded, n = 9,655); a low-lying conus (>=L3) occurred in only 0.05% (5/9,655), and remained rare (0.14%, 14/9,808) including operated examinations (median L1; mean 1.13 +/- 0.33). A slightly more cephalad position was seen with increasing age (negligible correlation). Among 475 preoperative children surgically treated for tethered cord, 99.6% had a normally positioned conus (<=L2) and only 0.4% were low-lying. Automated conus segmentation achieved a held-out Dice of 0.85. Conus radiomics likewise did not distinguish TCS from controls (equivalence-tested null; full segmentation/radiomics pipeline reported in the companion methodological paper). Conclusion. In children, the conus medullaris terminates at L1-L2 in more than 99% of cases and is normally positioned in virtually all children surgically treated for TCS. Within the conus, neither position nor texture (radiomics) identifies tethered cord; whether the filum terminale carries a diagnostic signal was not tested here.

Background and rationale: Knee osteoarthritis (KOA) is a leading cause of lower limb disability worldwide, characterized by functional limitations, stiffness and pain. The incidence of KOA is especially tied to age and obesity. It is a disabling disease that often makes patients less physically active, thus increasing the risk of other diseases and mortality1. The clinical diagnosis of KOA is based on the symptoms and functional limitations of the joint. The diagnosis is usually supported with a radiograph (X-ray) of the weight-bearing knee. Radiographic features, such as Kellgren-Lawrence grade, are used as eligibility criteria for clinical studies while other features, such as joint space width (JSW), are used as endpoints for structural KOA progression2,3. While the use of these radiographic features is standard in academia, the use of JSW as a structural biomarker has received criticism. Critics point out that JSW is an indirect and projection dependent measure of cartilage deterioration which is sensitive to technical factors such as the angulation of the X-ray beam and the positioning of the knee. Small differences in these factors can alter the measured joint space and may not reflect true disease progression4,5. Despite limitations, minimum joint space width (mJSW) remains as one of the most widely used structural biomarkers in KOA trials and is currently one of the only structural imaging accepted in regulatory guidance as evidence of disease modification in OA drug development3. For JSW to be reliable and consistent in determining the advancement of KOA, the use of fixed-flexion devices is crucial to reduce the risk of unwanted narrowing or widening of the radiographic joint space width6,7. The LOSEIT trial, which the present study is based on, acknowledges the angulation problem and uses a standard clinical fixed-flexion device in weight-bearing PA views to get reliable JSW results8. Historically, a radiologist would draw on and grade radiographs of the knee-joint to extract the features. However, manual reading and annotation is time consuming with notable interobserver variance9. With increasing computational power and the use of deep neural networks, off-the-shelf artificial intelligence (AI) tools have become available for automatic extraction of radiograph features. Automation would free up time from radiologists and provide more consistent measurements due to the reproducible nature of the models10. These tools have received regulatory approval for commercial use, however, regulatory approval does not guarantee uniform or bias free performance when used on real-world data11. Furthermore, in a large multi-hospital chest X-ray study, Zech et al., showed that convolutional neural networks achieved worse results on data from other hospitals than on the original hospitals in which it was tested12. This highlights the risk of overestimating the accuracy of AI tools when only internally validated. It is therefore apparent that external validation is required when testing these AI models. Objectives: The aim of this analysis is to evaluate the agreement of a commercially available AI tool for measuring JSW with the best practice radiologist annotation in the tibiofemoral joint of the knee in radiographs stabilized with a fixed-flexion device and acquired as part of a clinical trial. Methods: This study is a secondary analysis of the data from the LOSEIT trial, a randomized, double-blind, placebo-controlled, single-center trial, where patients were randomized to either liraglutide or identically appearing placebo after an initial weight-loss period to investigate the effects on KOA. Radiographs of the tibiofemoral joint were acquired at enrollment (week -8) and at end-of-trial (week 52) for a total acquisition-to-acquisition time of 60 weeks13. The primary analysis will assess agreement between AI-derived and reference-derived change in JSW from enrolment to follow-up. Change will be calculated as follow-up minus enrolment separately for the AI tool and the reference measurement. The main measure of interest will be the change in medial minimal JSW (mmJSW), with change in lateral minimal JSW (lmJSW), medial fixed JSW (mfJSW) and lateral fixed JSW (lfJSW) as secondary measures. This study will follow an equivalence framework using the two one-sided tests (TOST) approach with a Bland-Altman analysis as the main outcome. The equivalence margin will be set at {delta} = 0.5 mm. Agreement consistent with equivalence will be considered established if the upper limit of the 95% confidence interval (95% CI) for the upper limit of agreement (LoA) and the lower limit of the 95% CI for the lower LoA are within the established margins. The reference JSW will be the average measurement of two independent resident radiologists. If there is a mismatch in the measurements of more than 0.40 mm between the two radiologists, the radiologists will re-annotate the case independently. If the difference remains greater than 0.40 mm, a musculoskeletal radiology consultant will review the radiograph and establish the reference JSW. The index test will be the measurements output by the AI tool. Populations: Patients aged 18 to 74 with symptomatic knee osteoarthritis, radiographically confirmed KL grade 1-3, with a BMI [&ge;]27, motivated for weight loss and in accordance with the LOSEIT trial inclusion criteria Further statistical details Sample size: Not applicable as this is a secondary analysis. Framework: This is an agreement study assessing the equivalence of a commercially available AI tool for radiographic evaluation of knee osteoarthritis with best practice radiologist measurements. Confidence intervals and P values: All 95% confidence intervals and P-values will be two-sided. Statistical software: SAS Studio and/or R version 4.2.2 (or newer).

Background: Obesity is becoming a global health crisis, and it leads to various metabolic disorders. Body mass index fails to differentiate fat mass from lean mass and systematically misclassifies adiposity risk - a limitation particularly pronounced in South Asian adults, who exhibit characteristically elevated visceral adiposity and reduced appendicular lean mass at a normal BMI. The 2025 Lancet Commission explicitly recommends direct adiposity measurement beyond BMI for obesity diagnosis. Weight loss interventions - whether dietary, behavioural, or pharmacological - are consistently associated with concurrent reductions in both fat mass and lean mass, making body composition monitoring essential beyond scale weight alone. Although DEXA is globally accepted as a gold standard for body composition analysis, the accessibility of DEXA is limited, particularly in resource-constrained low and middle-income countries such as India. BIA devices are a convenient low-cost option to DEXA and can be used for body composition analysis more frequently than a DEXA scan to provide longitudinal data. The aim of this study is to validate 8 electrode BIA devices as a viable alternative to DEXA scan for the South Asian population. Methods: A prospective cross-sectional validation study was conducted following ethics committee approval, with a priori sample size estimation ( = 0.05, power = 80%). Fifty-eight healthy adults (n=58) underwent three BIA measurements and one DEXA scan each. To ensure statistical independence, the three BIA readings per participant were averaged, yielding 58 final measurements for validation. Body fat percentage, lean mass and fat mass were evaluated using Python with statistical analyses like Bland Altman analysis, Pearson correlation, ICC and regression analysis. Results: In this BIA vs DEXA study, the Pearson correlation was strong across all three outcomes (fat%: r = 0.97; fat mass: r = 0.98; lean mass: r = 0.96), with ICC (2,1) values of 0.94, 0.97, and 0.91 confirming excellent absolute agreement. Mean absolute error was 3.40% for fat percentage, 1.96 kg for fat mass, and 3.37 kg for lean mass. BIA systematically underestimated body fat percentage (bias -1.96%, 95% CI: -2.91% to -1.01%; LoA: -9.04% to +5.12%) and fat mass (bias -0.72 kg, 95% CI: -1.38 to -0.07 kg; LoA: -5.59 to +4.14 kg), while overestimating lean mass by +3.08 kg (95% CI: +2.34 to +3.82 kg; LoA: -2.46 to +8.62 kg). Conclusions: The 8-electrode BIA device shows clinically acceptable agreement with DEXA for body composition assessment in healthy Indian adults. It offers a radiation-free, cost-effective, accessible, and portable alternative to DEXA, making it suitable for longitudinal monitoring and trend detection. The device is particularly valuable for obesity screening and for tracking body composition changes during weight loss interventions at the population level, addressing the critical need for accessible body composition assessment in resource-limited settings.

Background: Low-intensity focused ultrasound (LIFU) is an emerging noninvasive neuromodulation technique capable of targeting deep cortical and subcortical structures with high spatial precision. In healthy human volunteers, LIFU has demonstrated a favorable safety and tolerability profile across multiple studies. However, its safety and tolerability in clinical populations remains poorly characterized, representing a critical barrier to clinical translation. Here, we prospectively evaluate the safety and tolerability of LIFU targeting the left dorsal anterior insula (dAI) in patients with fibromyalgia (FM). Methods: In a single-blind, sham-controlled, within-subjects crossover design, 13 individuals with FM (43.1 +/- 13.2 years; 12 female) received 10 minutes of active LIFU (500 kHz, 1 kHz PRF, 36% duty cycle, 4.2 W/cm2 Isppa; 100 x 1-second pulse trains with a 5-second inter-train interval) targeting the left dorsal anterior insula (dAI) or sham on separate visits. Safety was evaluated through neuroradiological review of post vs. pre LIFU FLAIR MRI, quantitative voxel-wise FLAIR analysis, and patient report of symptoms (ROS). Tolerability was assessed using an experience assessment. Efficacy of the LIFU intervention was assessed using quantitative sensory testing (QST) including temporal summation of pain (TSP) and conditioned pain modulation (CPM). Results: Neuroradiological review identified no new evidence of edema, microhemorrhage, acute ischemia, or white matter injury on post-LIFU structural imaging. Quantitative FLAIR analysis using contralateral-mirror-referenced relative FLAIR (rFLAIR) showed no significant within-subject change in the stimulated beam volume (delta rFLAIR = 0.002 +/- 0.025, t(12) = 0.30, P = 0.769, Cohen's dz = 0.08). No serious adverse events were documented and ROS indicated no change due to LIFU sonication. Participants rated the procedure as comfortable and could not distinguish active from sham LIFU. LIFU did not result in statistically significant changes for TSP (p = 0.797) or CPM (p = 0.465). Conclusions: Ten minutes of LIFU targeting the left dAI was safe and well tolerated in individuals with FM, with no neuroradiological or quantitative MRI evidence of tissue effects and no serious adverse events. Blinding was preserved, and participants rated the procedure as comfortable. Although no significant changes were observed in experimental pain measures, these findings support the feasibility of targeting deep salience and pain amplification circuitry with LIFU in patients with FM and provide a foundation for adequately powered efficacy trials.

This study aims to assess women's perceptions of artificial intelligence (AI) used in breast cancer screening in France by examining their knowledge of AI and the barriers to their participation in organized screening. The results of a survey conducted in June 2025 among a national sample of 2000 women (aged 40-75) reveal limited participation and persistent concerns among women. Nevertheless, despite a low awareness of specific AI applications, a large majority of the women surveyed are very favorable to the use of AI in breast cancer diagnosis, even considering it a lever to increase screening participation.

Background The European Association for the Study of the Liver (ESAL) - Steatotic Liver Disease (SLD) screening algorithm involves two steps; initial screening with FIB-4 followed by referral for vibration-controlled transient elastography (VCTE) in patients likely to have significant fibrosis (SF). However, VCTE is not widely available in resource-limited settings. Aim To optimise the EASL SLD screening algorithm for resource-poor settings using machine learning (ML). Methods We analysed data from 964 adults aged [&ge;]35 years who underwent VCTE at a tertiary referral centre in Sri Lanka between November 2024 and 2025. Multiple ML models using different methods and variable combinations were trained on 80% of the dataset and tested on the remaining 20%. Best models were selected based on performance and externally validated using data from 430 patients who underwent VCTE before November 2024. Model performance was compared with the FIB-4 using confusion matrices. Results A Random Forest model incorporating age, AST, ALT, and platelet count separately, rather than using FIB-4, outperformed. The all-variable ML model showed the best predictive performance for SF, with accuracy of 77.2%, recall of 0.762, precision of 0.778, and AUC-ROC of 0.818. The variables used in the model, in descending order of feature importance, were AST, platelet count, BMI, ALT, age, diabetes mellitus, hypertension, dyslipidaemia, sex, family history, hypothyroidism, diabetes complication and smoking. External validation demonstrated 75.1% accuracy and an AUC of 0.779. When used as the first step of the SLD screening algorithm, the all-variable ML model identified 37 (17.1%) additional true positives and reduced false-negative diagnoses by 50% compared with FIB-4. Conclusions ML-based models were more effective than the FIB-4 score as the first-line screening tool for VCTE referral, substantially improving the identification of patients with significant fibrosis in this South Asian cohort.

Introduction: Aphasia is an acquired language disorder with a significant negative functional impact. Much of the research on aphasia has focused on word-level language comprehension and production. Further evaluation of discourse-level tasks, both at behavioral and neural levels, will allow for an ecologically valid understanding of the functional implications of language impairment in this population. Method: This study evaluated bilateral frontal, temporal, and parietal cortical activity during computer-based narrative production in 14 young neurotypical individuals, 17 individuals with post-stroke aphasia, and 15 age-matched neurotypical participants using functional near-infrared spectroscopy (fNIRS). Oxygenated hemoglobin (HbO) was measured during narrative production following short video clips and compared to HbO during counting aloud. In addition, behavioral measures quantifying in-task performance were correlated with averaged HbO values. Results: Young neurotypical individuals showed greater cortical activity in bilateral language regions for narrative production compared to counting aloud. In contrast, people with aphasia showed positive condition-related effects in the right frontal ROI and the age-matched group showed positive condition-related effects in the left frontal and right precentral ROIs. Each group showed different patterns in relationships between cortical activity and discourse performance measures. Conclusion: Overall, young participants showing more consistent condition-related effects for narrative discourse production than individuals with aphasia and age-matched controls. This study shows the potential for fNIRS to evaluate cortical activity for ecologically valid language tasks in individuals with post-stroke aphasia.

Background Slow walking in older adults with mild parkinsonian signs (MPS) is a complex, multifactorial phenomenon arising from the cumulative burden of subclinical age-associated pathologies. This decline reflects age-associated neuronal loss in the dopaminergic system. A recent study suggests that levodopa treatment may enhance gait parameters. The goal of this small pilot study is to explore the effect of levodopa treatment on slow walking gait in older adults with MPS. Method This study was a randomized, placebo-controlled clinical pilot trial. Slow walking older adults without clinical evidence of PD were recruited and randomized into 2 groups (active treatment group or placebo control group). Participants in the active group were pre-treated with carbidopa for three days, followed by carbidopa-levodopa for seven days. Spatiotemporal gait parameters were evaluated at baseline and post-intervention. Results Gait factor analysis identified three main factors explaining gait characteristics at baseline, which included gait efficiency, gait rhythmicity, and gait turning.No effect of treatment was observed in the placebo group (p=0.111, p=0.616), no group difference was observed between the placebo and active group at baseline ({beta}=0.310, p=0.547), but a strong trend for a treatment-related increase was observed in the active treatment group ({beta}=0.506, p=0.076). Conclusion Our preliminary data suggest that sustained levodopa treatment (one week) in conjunction with carbidopa pre-treatment and concomitant carbidopa supplementation is feasible in slow walking older adults with MPS. Moreover, the data indicate potential efficacy, showing improvements in cadence, and step durations.

The purpose of this work is to analyze the 2-year overall survival (OS2y) of limited-stage small cell lung cancer (LS-SCLC) treated with chemoradiotherapy (CRT), aiming at characterizing the response of LS-SCLC, and in particular the /{beta} value and proliferation parameters. Through a systematic analysis of the literature, we collated a dataset containing 57 entries (3363 patients) of response of LS-SCLC treated with CRT. Radiotherapy schedules ranged from hyper- to hypofractionation. Four radiobiological models to describe the OS2y were investigated, with progressive levels of complexity including the effect of radiotherapy, chemotherapy, treatment year and toxicity. The Akaike Information Criterion (AIC) was used to compare models, and the profile likelihood methodology to compute confidence intervals. Model 4, which includes the effect of radiotherapy, chemotherapy, treatment year and dose-dependent toxicity, provided the best fits of the experimental data (lowest AIC value). While being the best model, model 4 still fails to provide a good prediction of the OS2y, in particular failing to predict the survival of the schedules achieving the lower/higher survivals. The radiobiological analysis of the dose-response of LS-SCLC to CRT does not allow to narrowly constrain the value of response parameters. We attribute this limitation to the large heterogeneity of this disease. Nonetheless, our analysis shows a large /{beta} value (>9 Gy, 95% CI), which implies a low fractionation effect in the radiotherapy of LS-SCLC. and an accelerated proliferation of tumor cells, {lambda}' > 1.6 Gy/day (95% CI), after a kick-off time of ~4-5 weeks, which supports the use of accelerated protocols to avoid the effect of tumor proliferation on the clinical outcome.

Mosquito-borne diseases continue to pose significant public health challenges worldwide, particularly in densely populated regions of South Asia and parts of North America experiencing increasing vector prevalence due to climate and environmental changes. Commercial mosquito repellents are widely used as a primary preventive measure; however, their efficacy, safety, and public health impacts vary depending on formulation, active ingredients, environmental conditions, and user practices. This study presents a comparative evaluation of commonly used mosquito repellent products in South Asia and North America, including coils, vaporizers, sprays, creams, and Natural repellents. The research aims to assess repellent efficacy against major mosquito vectors, evaluate potential health and respiratory effects associated with prolonged exposure, and analyze consumer awareness and usage patterns across different regions. Laboratory-based efficacy testing and field observations were conducted to compare protection duration, repellency rate, and environmental performance under varying climatic conditions. Safety assessments included analysis of chemical composition, indoor air quality impact, and reported adverse health symptoms among users. The findings indicate significant differences in effectiveness and safety profiles among product categories and geographical regions. Synthetic repellents generally demonstrated higher repellency duration, while herbal formulations showed improved safety and environmental compatibility. The study highlights the importance of standardized evaluation protocols, regulatory oversight, and public awareness in promoting safe and effective mosquito control strategies. These findings may support policymakers, healthcare professionals, and manufacturers in improving mosquito repellent technologies and reducing the burden of mosquito-borne diseases globally.

Background: Cognitive impairment in Parkinson's disease (PD) is linked to degeneration of the cholinergic basal forebrain, particularly cholinergic nucleus 4 (Ch4) in the nucleus basalis of Meynert. Structural and diffusion MRI separately detect this degeneration, but few studies have combined these modalities across the PD cognitive spectrum. Methods: We analyzed 92 participants: 14 healthy controls (HC), 35 PD with normal cognition (PD-NC), 33 with mild cognitive impairment (PD-MCI), and 10 with dementia (PDD). For Ch4 and cholinergic nuclei 1, 2, and 3 (Ch1-3) in the medial septal/diagonal band complex, we determined TIV-normalized gray matter density (GMD) and free-water (FW) fraction. We evaluated group differences, cognitive correlations, adjusted multivariable regression, and exploratory ROC discrimination. Results: Ch4 GMD was significantly lower in PDD compared to PD-MCI (p=0.007), PD-NC (p<0.001), and HC (p<0.001). Ch4 GMD was also lower in PD-MCI versus HC (p=0.028); the PD-MCI versus PD-NC difference was not significant after correction (p=0.074). Ch1-3 GMD was lower in PDD versus PD-NC (p=0.008) and HC (p=0.009). Ch4 and Ch1-3 FW were elevated in PDD versus all other groups (all p<0.01). Among PD patients (n=78), MoCA was positively correlated with Ch4 GMD ({rho}=0.49) and Ch1-3 GMD ({rho}=0.42) and negatively correlated with Ch4 FW ({rho}=-0.51) and Ch1-3 FW ({rho}=-0.40; all p<0.001). In the full four-metric model, Ch4 GMD and Ch4 FW were the only independent basal forebrain predictors (Ch4 GMD {beta}=+2.04, p<0.001; Ch4 FW {beta}=-1.46, p=0.005) of MoCA score. The combined Ch4 GMD + Ch4 FW model showed high discrimination for PDD versus non-demented PD (AUC=0.934; optimism-corrected AUC=0.925). Conclusions: Structural and free-water diffusion MRI provide complementary information about Ch4 degeneration in PD. The combined Ch4 model showed promising exploratory discrimination of PDD; validation in larger independent samples is needed.

Background: Repetitive Transcranial Magnetic Stimulation (rTMS) is a promising treatment across addictive disorders including Cannabis Use Disorder (CUD). Targeting incentive-salience circuitry via the ventromedial prefrontal cortex (vmPFC) and central-executive circuitry via the left dorsolateral prefrontal cortex (LDLPFC) are both promising treatment approaches; however, to date structural targets have predominated whereas functional targeting may allow for more precision. In this pilot trial we adapted a functional Magnetic Resonance Imaging (fMRI) Regulation of Craving (ROC) task to generate fMRI-based rTMS targets in the vmPFC and LDLPFC. Methods: We recruited treatment-seeking participants with moderate or severe CUD as a part of an open-label trial and administered an adapted ROC-task during fMRI following 24-hours of cannabis abstinence. We identified sub-portions of maximal activation of the LDLPFC when participants thought of long-term consequences of cannabis use (Later) and of the vmPFC when participants thought of short-term positive aspects of cannabis use (Now). We hypothesized that our task would generate acceptable rTMS targets in >66% of baseline fMRI scans. Results: A total of 20-participants enrolled in the trial (50%F, age=33.3+9.8) and completed the baseline fMRI. The adapted ROC-task elicited group level activation in the LDLPFC and precuneus in the Later>Now and in the bilateral vmPFC, ACC, and striatum in the Now>Later contrast. Acceptable functional targets resolved in both the vmPFC and LDLPFC in 19 of 20 participants (one participant did not tolerate MRI). Conclusions: The adapted ROC-task elicits activation in incentive salience and central executive circuitry and can feasibly generate rTMS targets when using a cluster selection algorithm.

Background: Cardiovascular-kidney-metabolic (CKM) syndrome is a leading driver of cardiovascular morbidity and mortality. Whole-body molecular imaging is well-positioned to phenotype such syndromes, yet no imaging biomarker quantifies cumulative CKM burden. Bone scintigraphy with 99mTc-labeled bisphosphonates is widely performed and expanding with transthyretin amyloidosis assessment, under which Perugini grade 0 (absent cardiac uptake) is considered clinically benign. Objective: We hypothesized that the soft tissue-to-bone ratio (STBR) on these scans captures CKM burden and is an independent prognostic biomarker. Methods: We retrospectively analyzed 8,769 consecutive patients without cardiac uptake on 99mTc-DPD whole-body planar scintigraphy. The primary endpoint was all-cause mortality. Secondary endpoints were major adverse cardiovascular events (MACE) and heart failure hospitalization. Cox models were adjusted for ten established cardiovascular risk factors. Imaging-phenotype association (IPA) analysis mapped STBR to 1,210 clinical traits. STBR distribution across CKM stages was assessed in four prespecified analyses, including a non-cancer subgroup. Results: During a median follow-up of 5.1 years (IQR 2.5-8.2), 2,418 deaths occurred. Patients with prespecified STBR >0.5 (n=772, 8.8%) had significantly higher mortality (adjHR 1.73, 95% CI 1.54-1.94, p<0.0001) with an adjHR of up to 3.42 at higher thresholds (95% CI 2.05-5.42, p<0.0001). Hazard increased monotonically with STBR. STBR >0.5 was independently associated with MACE (adjHR 1.51, 95% CI 1.11-2.05, p=0.008) and heart failure hospitalization (adjHR 1.31, 95% CI 1.02-1.67, p=0.03). The association was robust across all prespecified subgroups and sensitivity analyses, including continuous STBR and patients without renal insufficiency. IPA analysis identified significant associations with type 2 diabetes, chronic kidney disease, chronic ischaemic heart disease, heart failure, atrial fibrillation, liver disease, amyloidosis, and hypertension among binary traits, as well as with CRP, NT-proBNP, BUN, cholesterol (inverse), and hemoglobin (inverse) among continuous parameters. STBR increased monotonically across CKM stages in all sensitivity analyses (all p<0.0001). Conclusions: STBR derived from routine 99mTc-DPD bone scintigraphy in patients without cardiac uptake is an independent prognostic imaging biomarker associated with cumulative cardiovascular-kidney-metabolic burden. As an opportunistic measure from scans already acquired at scale, STBR could refine CKM risk stratification at no additional cost, radiation, or acquisition time.

Background Thalassemia is one of the most common monogenic disorders worldwide, current screening strategies combining hematological testing with molecular assays still carry a risk of missed diagnoses and undesirable efficiency, particularly for complex structural variants and rare mutations. Methods In this prospective double-blind, multicenter cohort study of 3,842 participants (3,362 pregnant women and 480 male partners), we conducted a head-to-head comparison to systematically evaluate the incremental clinical value and detection performance of single-molecule nanopore sequencing in thalassemia (SMITH) against conventional hematological testing and next-generation sequencing (NGS). Findings The overall concordance rate between NGS and SMITH was 98.6% (3789/3842). The discrepant cases (n=53) were directly attributed to the superior detection capabilities of SMITH, which successfully identified complex structural rearrangements-including 45 -globin gene triplications and four HK alleles-that were missed by NGS. Furthermore, SMITH accurately detected four rare variants (c.134_135insT/, c.-22(C>T)/, {beta}N/{beta}c.316-290delinsAGGGCAATAATTT and {beta}3.5 kb deletion/{beta}N ) and resolved ten trans and three cis configurations within the globin gene allele. Clinically, these technical advantages translated to a 9.3% (5/54) increase in the detection rate of high-risk prenatal couples, effectively preventing one birth affected by moderate-to-severe thalassemia. Additionally, SMITH corrected a diagnostic discrepancy in one case (HK vs. -3.7), sparing the couple from an unnecessary invasive procedure. Interpretation Our findings demonstrate that SMITH provides a powerful platform for resolving globin gene rearrangements, detecting rare variants, and enabling direct haplotype phasing. By effectively eliminating diagnostic blind spots, SMITH is expected to become an optimal method for thalassemia prevention programs. Funding This study was supported by Chinese National Natural Science Foundation Projects 81760037 and 82271894.

Electronic health record (EHR) prediction models often summarize longitudinal histories as static patient-level features, which may omit potentially informative event ordering. We developed a simplified spike-timing-dependent plasticity (STDP)-inspired framework that represents asynchronous EHR data as sparse, directional transition features. The approach encodes whether one clinical event precedes another within prespecified temporal windows, preserving event identity, directionality, and approximate timing while retaining feature-level interpretability. We evaluated this framework in two retrospective prediction tasks with different temporal scales: incident acute kidney injury (AKI) prediction in 17,351 MIMIC-IV ICU stays and early postoperative recurrence prediction in 713 CUMC patients with pancreatic ductal adenocarcinoma (PDAC). Models were compared with static burden features (demographics, comorbidities, raw lab measurements) and in addition with STDP transitional feature sets using patient-level cross-validation and rolling prediction horizons. In AKI, a calibrated STDP ensemble model showed higher discrimination than static burden alone at the 24-hour decision snapshot for AKI by 72 hours, with AUROC 0.838 versus 0.800, and at 48 hours for near-term AKI prediction, with AUROC 0.868 versus 0.827. In PDAC, STDP transition features modestly improved Day -30 preoperative recurrence prediction, with AUROC 0.611 versus 0.587 and AUPRC 0.323 versus 0.318 for static burden and showed similar performance at Day 0 (7 days before recorded surgery date), with AUROC 0.681 and AUPRC 0.363. Decision-curve and feature analyses suggested that selected temporal transitions were clinically interpretable across renal, inflammatory, hepatobiliary, hematologic, glycemic, and nutritional trajectories. These findings suggest that STDP-inspired transition features may provide a practical, interpretable way to incorporate temporal ordering into EHR-based risk prediction across both acute and longitudinal settings

Introduction: Early hospital presentation after stroke onset is necessary for rapid assessment and access to time-dependent acute management. This study examined the correlates of late presentation for stroke care among patients recorded at a tertiary hospital in Ondo State, Nigeria. Methods: A retrospective records review was conducted using secondary data from the Stroke Registry of the University of Medical Sciences Teaching Hospital, radiology department records, referral notes, and ambulance records. Records of stroke cases documented within the preceding 24 months were reviewed. Late presentation was defined as hospital presentation more than four hours after symptom onset. Frequencies, chi-square tests, and modified Poisson regression with robust standard errors were used to estimate adjusted prevalence ratios. Results: The analysis included 371 stroke cases. Of these, 317 (85.4%) presented after four hours, and the median time to presentation was 24 hours (interquartile range: 9-72 hours). Late presentation differed significantly by employment status, first-contact route, and pathway complexity at bivariate analysis. After adjustment, non-hospital first contact remained strongly associated with late presentation: patients whose first documented contact was non-hospital-based had almost 3 times the prevalence of delay compared with those whose first contact was hospital-based (adjusted prevalence ratio = 2.89; 95% confidence interval: 2.15-3.90; p < 0.001). Conclusion: Late presentation was pervasive in this tertiary hospital record cohort and was primarily associated with the initial direction of care-seeking. Stroke response interventions should emphasise immediate hospital presentation and strengthen urgent referral from non-hospital first-contact points.